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Establishment and Validation of a Predictive Model for the Risk of Colorectal Advanced Adenomas

N

Nanjing Medical University

Status

Unknown

Conditions

Colorectal Adenoma

Study type

Observational

Funder types

Other

Identifiers

NCT05152082
2021-SR-365

Details and patient eligibility

About

The study aimed to analyze the risk factors of colorectal advanced adenoma and constructe a model to predict the high-risk individuals of harbouring colorectal advanced adenomas, so as to better identify screening participants and provide an important theoretical basis for the prevention of colorectal cancer.

Full description

A large cohort of eligible patients were included in the analysis, and classified into derivation and validation cohorts at a ratio of 7:3. Demographic and clinicopathological characteristics of participants were utilized to develop a prediction model for colorectal advanced polyps. In the derivation cohort, the LASSO regression method was applied to filter variables and multivariate logistic regression analysis was used to identify important predictors.A prediction model was established based on the results of multivariate logistic regression analysis. The predictive performance of the model was evaluated with respect to its discrimination, calibration and clinical usefulness.

Enrollment

3,000 estimated patients

Sex

All

Ages

18 to 75 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • Patients aged ≥18 and ≤75 years old. Patients with complete data on demography and clinicopathology.

Exclusion criteria

  • Patients with a history of inflammatory bowel disease, enterophthisis, familial adenomatous polyposis, P-J syndrome and intestinal lymphoma.

Patients with a previous history of colorectal neoplasm. Patients with a history of severe systemic diseases. Lack of complete clinical data for analysis.

Trial design

3,000 participants in 2 patient groups

patients with colorectal advanced adenomas
patients without colorectal advanced adenomas

Trial contacts and locations

1

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Central trial contact

Hong Zhu, MD

Data sourced from clinicaltrials.gov

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